{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"hn-46911873","slug":"agent-arena-test-how-manipulation-proof-your-ai-ag","name":"Agent Arena – Test How Manipulation-Proof Your AI Agent Is","type":"agent","url":"https://wiz.jock.pl/experiments/agent-arena/","page_url":"https://unfragile.ai/agent-arena-test-how-manipulation-proof-your-ai-ag","categories":["testing-quality"],"tags":["hackernews","show-hn"],"pricing":{"model":"unknown","free":false,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"hn-46911873__cap_0","uri":"capability://safety.moderation.adversarial.prompt.injection.testing","name":"adversarial-prompt-injection-testing","description":"Generates and executes adversarial prompts designed to manipulate AI agents into unintended behaviors, using a library of injection techniques (jailbreaks, role-play escapes, context confusion) to probe agent robustness. The system constructs multi-turn conversation sequences that attempt to override system instructions, extract sensitive information, or trigger policy violations, then evaluates whether the agent resists or succumbs to manipulation.","intents":["test whether my AI agent can be tricked into ignoring its safety guidelines","identify specific prompt injection vectors that work against my deployed agent","benchmark my agent's resistance to manipulation compared to baseline models","discover edge cases where my agent's instructions can be circumvented"],"best_for":["AI safety researchers validating agent robustness","teams deploying customer-facing AI agents who need red-team testing","LLM application developers hardening agents against adversarial inputs","security-conscious organizations evaluating third-party AI systems"],"limitations":["test coverage depends on breadth of injection technique library — novel attack vectors may not be detected","results are probabilistic; agent may resist same prompt on retry due to sampling temperature and randomness","cannot test against agents with rate-limiting or input filtering that blocks Arena's test harness","no persistent tracking of which specific injection techniques succeeded — only aggregate pass/fail per test run"],"requires":["API endpoint or deployed agent accessible via HTTP/REST","agent must accept text input and return text output","no authentication requirements or IP whitelisting that blocks Arena's test infrastructure"],"input_types":["text prompts (natural language)","agent endpoint URL","optional: system prompt or agent configuration for context"],"output_types":["structured test results (pass/fail per injection technique)","agent response transcripts showing successful/failed manipulations","vulnerability report identifying which injection vectors succeeded"],"categories":["safety-moderation","red-teaming"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hn-46911873__cap_1","uri":"capability://safety.moderation.multi.turn.conversation.manipulation.chains","name":"multi-turn-conversation-manipulation-chains","description":"Constructs multi-turn conversation sequences that progressively build context and trust before attempting manipulation, simulating realistic social engineering attacks where an agent is gradually led toward policy violations through seemingly innocent back-and-forth exchanges. Each turn is designed to incrementally shift the agent's perceived context or constraints, making later injection attempts more likely to succeed.","intents":["test whether my agent can be manipulated through gradual context shifting rather than direct attacks","evaluate agent behavior when adversarial intent is hidden across multiple conversation turns","identify whether my agent maintains consistent safety guardrails across long conversations","understand how agent memory or context window management affects vulnerability to multi-turn attacks"],"best_for":["teams building conversational AI systems (chatbots, customer service agents)","organizations deploying agents in long-running dialogue scenarios","safety researchers studying how agent robustness degrades over conversation length","developers optimizing context management to prevent manipulation accumulation"],"limitations":["multi-turn testing is computationally expensive — full test suite may take minutes to complete","agent responses must be deterministic or low-temperature to produce reproducible results across test runs","cannot test agents with conversation history limits shorter than the injection chain length","success metrics are subjective — determining whether subtle manipulation 'succeeded' requires manual review in many cases"],"requires":["agent API that maintains conversation state across multiple requests","agent must support at least 5-10 sequential turns without resetting context","stateful session management or conversation ID tracking on agent backend"],"input_types":["multi-turn prompt sequences (ordered list of user messages)","agent endpoint with session/conversation ID support"],"output_types":["conversation transcript showing all turns and agent responses","manipulation success indicator (whether final turn achieved unintended behavior)","turn-by-turn analysis showing where agent's safety guardrails weakened"],"categories":["safety-moderation","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hn-46911873__cap_2","uri":"capability://safety.moderation.agent.behavior.comparison.benchmarking","name":"agent-behavior-comparison-benchmarking","description":"Runs the same adversarial test suite against multiple agents (different models, configurations, or versions) and produces comparative metrics showing which agents are more manipulation-resistant. The system normalizes results across different agent types and generates leaderboards or ranking tables that quantify relative robustness, enabling teams to benchmark their agent against competitors or track improvements across versions.","intents":["compare my agent's manipulation resistance against competitors or alternative models","track whether my agent's robustness improved after safety fine-tuning or instruction updates","choose between multiple agent implementations based on empirical safety metrics","publish benchmarks showing my agent's security posture relative to industry baselines"],"best_for":["product teams evaluating multiple AI vendors or model providers","researchers publishing agent safety benchmarks","organizations tracking safety improvements across agent versions","teams making build-vs-buy decisions for AI agents based on security criteria"],"limitations":["benchmarks are only valid for agents tested in the same Arena session — external agents tested separately may have different environmental factors affecting results","comparison assumes all agents have similar capabilities and constraints; comparing a specialized agent against a general-purpose model may produce misleading rankings","no statistical significance testing — differences in pass rates may be within noise margin for probabilistic agents","leaderboards can incentivize gaming (e.g., agents trained specifically to resist Arena's known injection techniques rather than general robustness)"],"requires":["multiple agent endpoints accessible simultaneously","agents must have compatible input/output formats","sufficient API quota to run full test suite against all agents without rate-limiting"],"input_types":["list of agent endpoints to compare","test configuration (which injection techniques to run, number of iterations)"],"output_types":["comparative metrics table (pass rate, vulnerability count per agent)","leaderboard ranking agents by robustness score","visualization comparing agent performance across injection categories","detailed report showing which agents failed which specific tests"],"categories":["safety-moderation","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hn-46911873__cap_3","uri":"capability://safety.moderation.injection.technique.library.curation","name":"injection-technique-library-curation","description":"Maintains a curated, categorized library of adversarial prompt injection techniques (jailbreaks, role-play escapes, context confusion, authority impersonation, etc.) that are continuously updated based on emerging attack vectors discovered in the wild. Each technique is tagged with metadata (success rate, target model families, required context length) and can be selectively enabled/disabled for targeted testing, allowing teams to focus on specific vulnerability classes relevant to their deployment.","intents":["test my agent against the latest known prompt injection techniques without manually researching attacks","focus testing on specific injection categories relevant to my use case (e.g., only role-play escapes for a customer service agent)","understand which injection techniques are most effective against my agent type","stay updated on emerging manipulation vectors as new attacks are discovered"],"best_for":["teams without dedicated security research capacity who need current threat intelligence","organizations testing agents in specific domains (customer service, content moderation, financial advice) where certain injection types are more relevant","researchers studying which injection techniques are most effective across model families","developers building agent hardening strategies based on empirical vulnerability data"],"limitations":["library is only as good as curation process — zero-day or novel injection techniques not yet discovered won't be included","technique effectiveness varies dramatically by model family and training data — a jailbreak effective against GPT-4 may fail against Claude or Llama","library maintenance requires ongoing research effort; if Arena team stops updating, library becomes stale relative to emerging attacks","selective testing (enabling only certain techniques) may miss interactions between different injection vectors"],"requires":["access to Arena's injection technique library (may require authentication or subscription)","agent must support all input types required by selected techniques (e.g., role-play requires multi-turn support)"],"input_types":["filter criteria (injection category, target model family, difficulty level)","optional: custom injection prompts to add to library"],"output_types":["filtered list of applicable injection techniques","metadata per technique (success rate, description, example prompts)","categorized breakdown of techniques by type (jailbreak, role-play, etc.)"],"categories":["safety-moderation","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hn-46911873__cap_4","uri":"capability://safety.moderation.agent.vulnerability.report.generation","name":"agent-vulnerability-report-generation","description":"Automatically generates structured vulnerability reports after test execution, documenting which injection techniques succeeded, providing example prompts that triggered failures, and categorizing vulnerabilities by severity and type. Reports include remediation suggestions (e.g., 'add explicit instruction to refuse role-play scenarios') and track vulnerability history across test runs to show whether patches actually reduced attack surface.","intents":["get a clear summary of which specific vulnerabilities my agent has after testing","understand what example prompts triggered each vulnerability so I can debug the agent","prioritize which vulnerabilities to fix based on severity and exploitability","track whether my safety improvements actually reduced vulnerabilities in subsequent test runs"],"best_for":["development teams using Arena results to drive agent hardening roadmaps","security teams documenting agent vulnerabilities for compliance or audit purposes","organizations tracking safety metrics over time as agents are updated","teams sharing vulnerability reports with stakeholders or customers"],"limitations":["report generation is deterministic only if agent responses are deterministic; probabilistic agents may show different vulnerabilities on re-test","severity ratings are heuristic-based and may not reflect actual risk in specific deployment contexts","remediation suggestions are generic (e.g., 'add instruction') and may not be effective without domain-specific tuning","reports don't account for mitigations outside the agent itself (e.g., input filtering, output monitoring at application layer)"],"requires":["completed test run with detailed response logs","agent must have consistent behavior across multiple test iterations for reliable vulnerability tracking"],"input_types":["test execution results (injection prompts, agent responses, pass/fail status)"],"output_types":["structured vulnerability report (JSON or PDF)","example prompts that triggered each vulnerability","severity ratings per vulnerability","remediation suggestions","vulnerability trend chart showing changes across test runs"],"categories":["safety-moderation","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hn-46911873__cap_5","uri":"capability://safety.moderation.interactive.agent.testing.interface","name":"interactive-agent-testing-interface","description":"Provides a web-based UI where users can manually test their agents against adversarial prompts in real-time, seeing agent responses immediately and iteratively refining test cases. The interface supports both automated test suite execution and manual prompt crafting, allowing teams to explore edge cases and develop custom injection techniques specific to their agent's domain or instruction set.","intents":["manually test my agent against specific adversarial prompts I'm concerned about","interactively explore how my agent responds to variations of an injection technique","develop custom injection prompts tailored to my agent's specific instructions or domain","quickly iterate on agent hardening by testing changes in real-time without re-deploying"],"best_for":["developers actively hardening agents who need fast feedback loops","teams exploring edge cases specific to their agent's domain or instructions","non-technical stakeholders who want to understand agent vulnerabilities without reading raw logs","researchers developing novel injection techniques and needing interactive testing"],"limitations":["manual testing is not reproducible — results depend on user-crafted prompts which may not be systematic or comprehensive","interactive testing is slower than automated test suites for broad coverage","UI may not scale well for testing hundreds of agents or thousands of injection variants","manual testing introduces human bias — users may unconsciously avoid testing certain vulnerability classes"],"requires":["web browser with JavaScript support","agent endpoint accessible from Arena's infrastructure","low-latency network connection for interactive response times"],"input_types":["free-form text prompts entered via web UI","agent endpoint URL","optional: system prompt or agent configuration"],"output_types":["real-time agent response displayed in UI","conversation history showing all prompts and responses","optional: save test case for later automated execution"],"categories":["safety-moderation","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":35,"verified":false,"data_access_risk":"low","permissions":["API endpoint or deployed agent accessible via HTTP/REST","agent must accept text input and return text output","no authentication requirements or IP whitelisting that blocks Arena's test infrastructure","agent API that maintains conversation state across multiple requests","agent must support at least 5-10 sequential turns without resetting context","stateful session management or conversation ID tracking on agent backend","multiple agent endpoints accessible simultaneously","agents must have compatible input/output formats","sufficient API quota to run full test suite against all agents without rate-limiting","access to Arena's injection technique library (may require authentication or subscription)"],"failure_modes":["test coverage depends on breadth of injection technique library — novel attack vectors may not be detected","results are probabilistic; agent may resist same prompt on retry due to sampling temperature and randomness","cannot test against agents with rate-limiting or input filtering that blocks Arena's test harness","no persistent tracking of which specific injection techniques succeeded — only aggregate pass/fail per test run","multi-turn testing is computationally expensive — full test suite may take minutes to complete","agent responses must be deterministic or low-temperature to produce reproducible results across test runs","cannot test agents with conversation history limits shorter than the injection chain length","success metrics are subjective — determining whether subtle manipulation 'succeeded' requires manual review in many cases","benchmarks are only valid for agents tested in the same Arena session — external agents tested separately may have different environmental factors affecting results","comparison assumes all agents have similar capabilities and constraints; comparing a specialized agent against a general-purpose model may produce misleading rankings","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.46,"quality":0.22,"ecosystem":0.21000000000000002,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.1,"match_graph":0.28,"freshness":0.12}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:23.326Z","last_scraped_at":"2026-05-04T08:09:59.925Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=agent-arena-test-how-manipulation-proof-your-ai-ag","compare_url":"https://unfragile.ai/compare?artifact=agent-arena-test-how-manipulation-proof-your-ai-ag"}},"signature":"2Hmggq5ZSd6LZfjnij5dHQw5/ycM7YrALA8SCqu5z9M9HJ6SLboVQ9MVNsMSqrlNuuPW2rOBO0Flz1LjfjyECw==","signedAt":"2026-06-21T10:19:38.826Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/agent-arena-test-how-manipulation-proof-your-ai-ag","artifact":"https://unfragile.ai/agent-arena-test-how-manipulation-proof-your-ai-ag","verify":"https://unfragile.ai/api/v1/verify?slug=agent-arena-test-how-manipulation-proof-your-ai-ag","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}